Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences

__Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences__ brings together two very important fields in pharmaceutical sciences that have been mostly seen as diverging from each other: chemoinformatics and bioinformatics. As developing drugs is an expensive and lengthy process, technology can improve the cost, efficiency and speed at which new drugs can be discovered and tested. This book presents some of the growing advancements of technology in the field of drug development and how the computational approaches explained here can reduce the financial and experimental burden of the drug discovery process. This book will be useful to pharmaceutical science researchers and students who need basic knowledge of computational techniques relevant to their projects. Bioscientists, bioinformaticians, computational scientists, and other stakeholders from industry and academia will also find this book helpful. Front-Matte_2021_Chemoinformatics-and-Bioinformatics-in-the-Pharmaceutical-S Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences Copyright_2021_Chemoinformatics-and-Bioinformatics-in-the-Pharmaceutical-Sci Copyright Contributor_2021_Chemoinformatics-and-Bioinformatics-in-the-Pharmaceutical-S Contributors Chapter-1---Impact-of-chemoinformatics-a_2021_Chemoinformatics-and-Bioinform 1. Impact of chemoinformatics approaches and tools on current chemical research 1.1 Background 1.2 Ligand and target resources in chemoinformatics 1.2.1 Small molecule compound databases 1.2.2 Protein and ligand information databases 1.2.3 Databases related to macromolecular interactions 1.3 Pharmacophore modeling 1.3.1 Types of pharmacophore modeling 1.3.2 Scoring scheme and statistical approaches used in pharmacophore modeling 1.4 QSAR models 1.4.1 Methodologies used to build QSAR models 1.4.2 Fragment-based 2D-QSAR 1.4.3 3D-QSAR model 1.4.4 Multidimensional or 4D-QSAR models 1.4.5 Statistical methods for generation of QSAR models 1.4.6 Multivariate linear regression analysis 1.5 Docking methods 1.5.1 Scoring functions 1.5.2 Pose prediction 1.5.3 MD simulations 1.6 Conclusion Acknowledgments References Chapter-2---Structure--and-ligand-based-d_2021_Chemoinformatics-and-Bioinfor 2. Structure- and ligand-based drug design: concepts, approaches, and challenges 2.1 Introduction 2.1.1 Advantages of CADD 2.2 Ligand-based drug design 2.2.1 Molecular similarity-based search 2.2.1.1 Concept 2.2.1.2 Workflow 2.2.1.3 Applications 2.2.1.4 Challenges 2.2.2 Quantitative structure–activity relationship 2.2.2.1 Concept 2.2.2.2 Workflow 2.2.2.3 Tools 2.2.2.4 Applications 2.2.2.5 Challenges 2.2.3 Ligand-based pharmacophore 2.2.3.1 Concept 2.2.3.2 Workflow 2.2.4 Tools 2.2.4.1 Applications 2.2.5 Challenges 2.3 Structure-based drug design 2.3.1 Homology modeling 2.3.1.1 Concept 2.3.1.2 Workflow 2.3.1.3 Tools 2.3.2 Applications 2.3.3 Challenges 2.3.4 Molecular docking 2.3.4.1 Concept 2.3.4.2 Workflow 2.3.4.3 Tools and software 2.3.4.4 Applications 2.3.4.5 Challenges 2.3.5 Virtual screening 2.3.5.1 Concept 2.3.5.2 General workflow 2.3.5.3 Tools 2.3.5.4 Applications 2.3.5.5 Challenges 2.3.6 Receptor-based pharmacophore modeling 2.3.6.1 Concept 2.3.6.2 Workflow 2.3.6.3 Tools 2.3.6.4 Applications 2.3.6.5 Challenges 2.3.7 Molecular dynamics simulations 2.3.7.1 Concept 2.3.7.2 Workflow 2.3.7.3 Tools 2.3.7.4 Applications 2.3.7.5 Challenges References Chapter-3---Advances-in-structu_2021_Chemoinformatics-and-Bioinformatics-in- 3. Advances in structure-based drug design 3.1 Introduction 3.1.1 Structure-based drug design methods 3.2 Molecular docking 3.2.1 Challenges in docking 3.2.2 Types of molecular docking 3.2.2.1 Rigid versus flexible docking 3.2.2.2 Blind versus site-directed docking 3.2.3 Methodology 3.2.3.1 Generation of a 3D structure of receptor and ligand 3.2.3.2 Cleaning and refinement of structures 3.2.3.3 Identification of active site 3.2.3.4 Conformational flexibility of ligand and receptor 3.2.3.5 Docking 3.2.3.6 Analysis of docking results 3.2.4 Docking algorithms 3.2.4.1 Shape complementarity algorithm 3.2.4.2 Exhaustive systematic search algorithm 3.2.4.3 Fragment-based docking 3.2.4.4 Stochastic search algorithm 3.2.5 Scoring functions in docking 3.2.5.1 Forcefield-based scoring functions 3.2.5.2 Empirical scoring functions 3.2.5.3 Knowledge-based scoring function 3.2.5.4 Consensus scoring 3.3 High-throughput screening 3.3.1 Methodology of virtual screening 3.3.1.1 Compound databases 3.3.1.2 Ligand preparation of the compound database 3.3.1.2.1 Library design 3.3.1.3 Target preparation 3.3.1.4 Docking 3.3.1.5 Postprocessing 3.4 De novo ligand design 3.4.1 Whole molecule docking 3.4.2 Fragment-based methods 3.5 Biomolecular simulations 3.5.1 Molecular dynamics simulations 3.5.1.1 Accelerated molecular dynamics 3.5.1.2 Umbrella sampling 3.5.1.3 Metadynamics sampling 3.5.1.4 Targeted molecular dynamics 3.5.1.5 Parallel tempering method 3.5.2 Monte Carlo simulations 3.6 ADMET profiling 3.7 Conclusion References Chapter-4---Computational-tools_2021_Chemoinformatics-and-Bioinformatics-in- 4. Computational tools in cheminformatics 4.1 Introduction 4.2 Molecules and their reactions: representation 4.2.1 Data mining 4.2.2 Representation of chemical structures 4.2.3 Representation of chemical reactions 4.3 Preparation before building libraries for databases in cheminformatics 4.3.1 Importance of descriptors 4.3.2 Verification and manipulation of data 4.3.3 Development of computational models for designing a new drug 4.3.4 Similarity techniques 4.3.5 Selection of compounds based on diversity analysis 4.4 High-throughput screening and virtual screening 4.5 Combinatorial libraries 4.6 Additional computational tools in cheminformatics: molecular modeling 4.6.1 Molecular mechanics methods 4.6.2 Semiempirical methods 4.6.3 Ab initio methods 4.6.4 Density functional theory 4.6.5 Molecular dynamics 4.6.6 Monte Carlo simulations 4.6.6.1 Importance of molecular dynamics simulations 4.6.6.2 Contrast between molecular dynamics simulations and Monte Carlo simulations 4.6.7 Molecular docking 4.7 Conclusions References Further reading Chapter-5---Structure-based-drug-designing-st_2021_Chemoinformatics-and-Bioi 5. Structure-based drug designing strategy to inhibit protein-protein-interactions using in silico tools 5.1 Introduction 5.2 Methods to identify inhibitors of PPIs 5.3 Nature of the PPI interface 5.4 Computational drug designing 5.5 Databases that play a significant role in the process of predicting PPI inhibitors: databases of PPIs, PPI modulators, and ... 5.5.1 Databases of PPIs 5.5.2 Databases of PPI modulators 5.5.3 Decoy databases for PPIs and modulators 5.6 Transcription factors as one of the PPI drug targets: importance, case study, and specific databases 5.7 Pharmacokinetic properties of small-molecule inhibitors of PPI 5.8 Strategies and tools to identify small-molecule inhibitors of PPIs 5.8.1 Prediction of interacting residues and hot spots in protein–protein complexes 5.8.2 Screening of small molecules 5.8.3 Prediction of ADME/T properties 5.9 Conclusion References Chapter-6---Advanced-approaches-and-in-si_2021_Chemoinformatics-and-Bioinfor 6. Advanced approaches and in silico tools of chemoinformatics in drug designing 6.1 Introduction 6.2 Current chemoinformatics approaches and tools 6.2.1 Ligand databases/libraries 6.2.2 In silico structure-based virtual screening 6.2.2.1 Classes of molecular docking 6.2.2.2 Molecular docking tools 6.2.3 Pharmacophore development 6.2.3.1 Pharmacophore development tools 6.2.4 Quantitative structure–activity relationship prediction 6.2.4.1 Types of QSAR 6.2.4.2 QSAR modeling tools 6.3 Machine learning approaches and tools for chemoinformatics 6.3.1 Techniques of ML 6.3.2 Types of supervised learning 6.3.3 Algorithms for classification and regression problems in drug designing 6.4 Conclusion References Chapter-7---Chem-bioinformatic-approach-for-d_2021_Chemoinformatics-and-Bioi 7. Chem-bioinformatic approach for drug discovery: in silico screening of potential antimalarial compounds 7.1 Importance of technology in medical science 7.2 Origin of cheminformatics 7.2.1 Role of cheminformatics in drug designing 7.2.1.1 Selection of a compound library 7.2.1.2 Virtual screening 7.2.1.3 High-throughput screening 7.2.1.4 Structure–activity relationship on high-throughput screening data and sequential screening 7.2.1.5 In silico ADMET 7.3 Role of bioinformatics in drug discovery 7.3.1 In silico designing of a drug using the structure-based approach 7.3.1.1 Selection of the target 7.3.1.2 Evaluation of the drug target 7.3.1.3 Refining the target structure 7.3.1.4 Locating the binding site 7.3.1.5 Docking ligands into the binding site 7.3.2 In silico drug designing using the ligand-based approach 7.3.2.1 Pharmacophore modeling 7.3.2.2 Quantitative structure–activity relationship 7.3.3 Another exquisite tool: molecular dynamics 7.4 Applications of cheminformatics and bioinformatics in the development of antimalarial drugs 7.4.1 Background of the disease 7.4.2 Antimalarials commercially available 7.4.3 Hybrid molecules: an alternative to conventional antimalarial drugs 7.4.4 Computational details 7.4.4.1 Collection of dataset 7.4.4.2 Steps involved in pharmacophore and 3D QSAR model building 7.4.4.3 Preparation of ligands 7.4.4.4 Site creation and finding pharmacophores 7.4.4.5 Scoring of pharmacophores 7.4.4.6 Model validation: 3D QSAR 7.4.4.7 Creating a virtual library 7.4.4.8 Molecular docking 7.4.4.9 In silico rapid ADME prognosis 7.4.5 Results and discussion 7.4.5.1 3D QSAR visualization 7.4.5.2 Virtual database screening 7.4.5.3 Drug resemblance analysis 7.4.5.4 Docking of lead molecules with Fe(III)PPIX ring 7.4.5.5 Docking of lead molecules with pf-DHFR 7.5 Conclusions Electronic Supplementary information Chem-bioinformatic approach for drug discovery: in silico screening of potential antimalarial compounds Acknowledgments References Chapter-8---Mapping-genomes-by-usin_2021_Chemoinformatics-and-Bioinformatics 8. Mapping genomes by using bioinformatics data and tools 8.1 Background 8.1.1 Emergence and evolution of bioinformatics 8.2 Genome 8.2.1 Gene expression 8.2.2 Gene prediction 8.3 Sequence analysis 8.3.1 Nucleotide sequence analysis 8.3.2 Protein sequence analysis 8.3.3 Phylogenetic analyses 8.3.3.1 Distance-based method 8.3.3.2 Character-based method 8.4 Sequence database 8.4.1 Genomic database 8.4.1.1 Advantages of the genomic database are 8.4.1.2 GenBank 8.4.1.3 SGD 8.4.1.4 Other genomic databases 8.4.2 Protein sequence databases 8.4.2.1 Types of protein sequence databases 8.4.2.2 Protein sequence archives 8.4.2.3 Universal curated database 8.4.2.3.1 Swiss-Prot 8.4.2.3.1 Swiss-Prot 8.4.2.3.2 TrEMBL 8.4.2.3.2 TrEMBL 8.4.2.3.3 UniProt 8.4.2.3.3 UniProt 8.5 Structure prediction 8.5.1 Template-based modeling 8.5.1.1 Homology modeling 8.5.1.2 Protein threading 8.5.2 Template-free modeling 8.5.2.1 Ab initio protein modeling 8.6 Bioinformatics and drug discovery 8.6.1 Drug target identification 8.6.2 Drug target validation 8.6.3 Lead identification and optimization 8.7 Pharmacogenomics 8.8 Future aspects References Chapter-9---Python--a-reliable-programmin_2021_Chemoinformatics-and-Bioinfor 9. Python, a reliable programming language for chemoinformatics and bioinformatics 9.1 Introduction 9.2 Desired skill sets 9.3 Python 9.4 Python in bioinformatics and chemoinformatics 9.5 Use Python interactively 9.6 Prerequisites to working with Python 9.6.1 Linux OS/OSX 9.6.2 Basic Linux bash commands 9.6.3 Anaconda 9.6.4 Installing Python in the conda environment 9.6.5 Jupyter Notebook 9.7 Quick overview of Python components 9.7.1 Variable 9.7.2 Operators in Python 9.7.3 Control flow and control statements in Python 9.7.4 Python functions 9.7.5 Library or a module 9.7.6 Indentation 9.7.7 Data structure 9.8 Bioinformatics and cheminformatics examples 9.8.1 Genomics data handling and analysis 9.8.2 Chemoinformatics data handling and analysis 9.9 Conclusion References Chapter-10---Unveiling-the-molecular-basis_2021_Chemoinformatics-and-Bioinfo 10. Unveiling the molecular basis of DNA–protein structure and function: an in silico view 10.1 Background 10.2 Structural aspects of DNA 10.2.1 DNA: structural elements 10.2.2 DNA: nitrogenous bases of DNA are involved in base pairing 10.3 Structural aspects of proteins 10.3.1 Characteristic features of amino acids 10.3.2 Characteristic features of proteins 10.3.3 Classification of protein-binding motifs 10.4 In silico tools for unveiling the mystery of DNA–protein interactions 10.4.1 TRANSFAC 10.4.2 DISPLAR (DNA site prediction from record of neighboring residues) 10.4.3 iDBPs (exploration of DNA-binding proteins) 10.4.4 MAPPER (multigenome analysis of position and patterns of elements of regulation) 10.4.5 DP-Bind 10.4.6 PreDs 10.4.7 ZIFIBI (zinc finger site database) 10.4.8 Bindn and Bindn+ 10.4.9 ProNIT 10.4.10 DNA-Prot 10.4.11 PDIdb 10.4.12 PADA1 (protein assisted DNA assembly 1) 10.4.13 DNAproDB 10.4.14 WebPDA 10.4.15 DOMMINO 10.4.16 FlyFactorSurvey 10.5 Future perspectives 10.6 Abbreviations References Chapter-11---Computational-c_2021_Chemoinformatics-and-Bioinformatics-in-the 11. Computational cancer genomics 11.1 Introduction 11.2 Cancer genomics technologies 11.3 Computational cancer genomics analysis 11.3.1 Mapping and alignment 11.3.2 RNA-seq data for pan-cancer 11.3.3 Databases 11.3.4 Genomics landscape for oncogenic mutations 11.3.4.1 Germline mutations 11.3.4.2 Somatic mutations 11.3.4.2.1 Somatic mutations in pan-cancer 11.3.4.2.1 Somatic mutations in pan-cancer 11.3.5 Noncoding mutations 11.3.6 Variant annotation 11.3.7 Structural variants 11.4 Pathway analysis 11.5 Network analysis 11.5.1 Data integration and methodological combination 11.5.2 Software resources (workflow and visualization interfaces) 11.6 Conclusion References Chapter-12---Computational-and-functional-_2021_Chemoinformatics-and-Bioinfo 12. Computational and functional annotation at genomic scale: gene expression and analysis 12.1 Introduction: background (history) 12.2 Genome sequencing 12.2.1 First generation (Sanger’s generation): an old but reliable approach 12.2.2 Second-generation/next-generation sequencing 12.2.2.1 454 (Roche) sequencing 12.2.2.2 Illumina sequencing 12.2.2.3 Ion Torrent sequencing 12.2.2.4 SOLiD sequencing 12.2.3 Third generation (current generation) 12.2.3.1 PacBio 12.2.3.2 Oxford Nanopore 12.3 Genome assembly 12.3.1 De novo assembly 12.3.2 Reference assembly 12.4 Genome annotation 12.4.1 Levels of genome annotation 12.4.1.1 Nucleotide level 12.4.1.2 Protein level 12.4.1.3 Process level 12.4.2 Tools for genome annotation 12.4.3 Reliability of genome annotation 12.5 Techniques for gene expression analysis 12.5.1 SAGE 12.5.2 DNA microarray 12.5.3 RNA-seq 12.6 Gene expression data analysis 12.6.1 Data analysis by data mining 12.6.1.1 Clustering method 12.6.1.1.1 Hierarchical 12.6.1.1.1 Hierarchical 12.6.1.1.2 Partitioned 12.6.1.1.2 Partitioned 12.6.1.1.3 Model based 12.6.1.1.3 Model based 12.6.1.2 Classification methods 12.6.1.2.1 KNN 12.6.1.2.1 KNN 12.6.1.2.2 SVM 12.6.1.2.2 SVM 12.6.1.2.3 DT 12.6.1.2.3 DT 12.6.2 Data analysis by ontology 12.7 Software for gene expression analysis 12.8 Computational methods for clinical genomics 12.9 Conclusion Abbreviations References Chapter-13---Computational-methods--in-sili_2021_Chemoinformatics-and-Bioinf 13. Computational methods (in silico) and stem cells as alternatives to animals in research 13.1 Introduction 13.2 Need for alternatives 13.3 What are the alternative methods to animal research 13.3.1 Physicochemical techniques 13.3.2 Cell and tissue culture 13.3.3 Tissue engineering 13.3.4 Microbiological analysis 13.3.5 Mathematical models and computer simulations 13.3.6 Epidemiological surveys 13.3.7 Plant analysis 13.3.8 Microdosing 13.3.9 Microfluidics chips 13.3.10 Tissue chips in space 13.3.11 Noninvasive imaging techniques 13.4 Potential of in silico and stem cell methods to sustain 3Rs 13.4.1 In silico 13.4.1.1 BLAST (basic local alignment search tool) 13.4.1.2 Multiple sequence alignment tools 13.4.1.3 Structure–activity relationship 13.4.1.4 Molecular modeling 13.4.1.5 Computer simulation in organ modeling 13.4.1.6 Molecular docking 13.4.1.7 Structure-based virtual screening 13.4.1.8 Microarray or DNA-based chip 13.4.1.9 Microarray data analysis 13.4.1.10 Artificial intelligence and machine learning 13.4.1.11 In silico has the edge over animal testing 13.4.2 Stem cells: an emerging alternative to animal research 13.4.2.1 Stem cells and their types 13.4.2.2 Stem cells as a promising alternative 13.4.2.3 Shortcomings of stem cells 13.5 Challenges with alternatives 13.6 Conclusion Acknowledgments References Chapter-14---An-introduction-to-BLAST--app_2021_Chemoinformatics-and-Bioinfo 14. An introduction to BLAST: applications for computer-aided drug design and development 14.1 Basic local alignment search tool 14.2 Building blocks 14.2.1 Sequence alignment 14.2.2 Note 14.2.3 Types of mutations 14.2.4 Scoring matrices 14.2.5 Dynamic programming 14.3 Basic local alignment search tool 14.4 How BLAST works 14.5 Codons, reading frames, and open reading frames 14.6 Bioinformatics and drug design 14.7 Applications of BLAST 14.8 Understanding coronavirus: the menace of 2020 14.8.1 BLAST simulation practical 14.9 Conclusions References Chapter-15---Pseudoternary-phase-diag_2021_Chemoinformatics-and-Bioinformati 15. Pseudoternary phase diagrams used in emulsion preparation 15.1 Introduction 15.2 Classification of emulsions 15.2.1 Simple emulsion 15.2.2 Complex/multiple emulsion 15.2.3 Macroemulsion 15.2.4 Microemulsion 15.2.5 Nanoemulsion 15.3 Emulsifying agents (surfactants) 15.4 Pseudoternary phase diagrams 15.4.1 Phase behavior 15.4.2 Understanding of the pseudoternary phase diagram 15.4.3 How to plot values on the triangle of the pseudoternary phase diagram 15.4.4 Preparation of the pseudoternary phase diagram 15.4.4.1 Preparation of surfactant mix (Smix) 15.4.4.2 Mixing of surfactant (Smix) and oil in a defined ratio 15.4.4.3 Determination of equilibrium point 15.4.4.4 Preparation of ternary phase diagrams 15.5 Software used for the preparation of pseudoternary phase diagrams 15.5.1 Chemix School 15.5.2 XL stat 15.6 Conclusion References Index_2021_Chemoinformatics-and-Bioinformatics-in-the-Pharmaceutical-Science Index A B C D E F G H I J K L M N O P Q R S T U V W X Z Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences brings together two very important fields in pharmaceutical sciences that have been mostly seen as diverging from each other: chemoinformatics and bioinformatics. As developing drugs is an expensive and lengthy process, technology can improve the cost, efficiency and speed at which new drugs can be discovered and tested. This book presents some of the growing advancements of technology in the field of drug development and how the computational approaches explained here can reduce the financial and experimental burden of the drug discovery process. This book will be useful to pharmaceutical science researchers and students who need basic knowledge of computational techniques relevant to their projects. Bioscientists, bioinformaticians, computational scientists, and other stakeholders from industry and academia will also find this book helpful.- Provides practical information on how to choose and use appropriate computational tools- Presents the wide, intersecting fields of chemo-bio-informatics in an easily-accessible format- Explores the fundamentals of the emerging field of chemoinformatics and bioinformatics